A time for learning: Representations of time and the temporal dimensions of learning through the lifecourse.

For nearest neighbor time series classication, we propose to learn a metric as a combination of temporal and frequential metrics based on a large margin opti- mization process..

Similarly to what has been done for the time coordinate, the expression of the time-frequency representation at point pt, ν q as the sum of a mass function Φ t,ν , proved in

It consists in a multi- class single label classification problem where the examples to classify are pixels described by the time series of satellite images and the prediction

Using this partition of the subsystems and particularly exploiting the time margins corresponding to idle times of machines, the approach allows an optimization of the non-critical

We found that pitch and additive noise are the most powerful data augmentation tech- niques for our implementation of contrastive predictive coding, yielding very good results

Our first remark is the fact that time-frequency representations are generally functions defined on a two-dimensional space, called the phase space, or the time- frequency space,

Since literary representations never depict reality as it is in regard to this point, it can, according to Aristotle, reach a higher grade of universality and logic and thus may

We show that the Tolman-Ehrenfest effect (in a stationary gravitational field, temperature is not constant in space at thermal equilibrium) can be derived very simply by applying